[unreadable] This proposal is an application for a K08 award for Dr. Scott Biggins, a transplant hepatologist at the University of California, San Francisco (UCSF). Dr. Biggins is establishing himself as [sic] young clinical-scientist investigating evidence-based improvements in transplant recipient selection and donor organ allocation systems. This award will provide Dr. Biggins with the support necessary to accomplish the following goals: (1) develop expertise in outcome modeling of end-stage liver disease and liver transplantation; (2) investigate the novel application of marginal structural models (MSM) to enhance the survival benefit from scarce donor organs; and (3) implement applied mathematics and medical ethics in organ allocation policymaking. To achieve these goals, Dr. Biggins has assembled a mentoring team comprised of a primary mentor: Dr. John Inadomi, Director of the Health Outcomes Policy and Economics (HOPE) Program, who conducts research to optimize use of the clinical and financial resources in colorectal cancer screening; two co-mentors: Dr Norah Terrault, Director of Hepatitis Research in Liver Transplantation and Dr. John Roberts, Chief of Transplantation Surgery; and two consultants: Dr. Peter Bacchetti, Director of the Biostatistical Consulting Unit and Dr. Bernard Lo, Director of Medical Ethics. At present, liver grafts are allocated for retransplantation using the identical protocol as for initial transplantation. This standard protocol is based on predicted pre-transplant mortality (urgency) using the MELD score. To optimize use of these scarce organs, policymakers now advocate for incorporation of predicted post-procedure survival (outcome) into the allocation of liver grafts for retransplantation. Yet, current retransplantation outcome models are susceptible to bias inherent in the candidate selection (listing) criteria, particularly with respect to patients infected with hepatitis C virus. Dr. Biggins will develop a comprehensive retrospective (N=1022) and prospective (N=107) database of prior liver transplant recipients with liver graft dysfunction to identify factors that predict listing for retransplantation (Aim 1), evaluate the impact of the selection bias in current outcome models predicting post-retransplantation survival (Aim 2), develop new outcome models avoiding bias from listing and maximize the survival benefit of retransplantation (Aim 3), and develop a prospective cohort of potential retransplantation candidates for further refinement of candidate selection criteria and liver allocation (Aim 4). Unlike prior modeling studies that used only a subset of potential retransplant candidates (candidates who are listed or who have undergone retransplantation), Dr. Biggins will use the novel statistical application of inverse probability weighting, also known as marginal structural models, to expand the candidate population to include all potential retransplantation candidates with liver graft dysfunction. Public health relevance: Optimized candidate selection and allocation of livers for retransplantation will limit the misuse of a life-saving and scarce resource. [unreadable] [unreadable]